Guided data augmentation for improved semi-supervised image classification in low data regime.

Deep learning models have achieved state of the art performances, especially for computer vision applications. Much of the recent successes can be attributed to the existence of large, high quality, labeled datasets. However, in many real-world applications, collecting similar datasets is often cumbersome and time consuming. For instance, developing robust automatic target recognition models from infrared images still faces major challenges. This is mainly due to the difficulty of acquiring high resolution inputs, sensitivity to the thermal sensors\u27 calibration, meteorological conditions, targets\u27 scale and viewpoint invariance. Ideally, a good training set should contain enough variations within each class for the model to learn the most optimal decision boundaries. However, when there are under-represented regions in the training feature space, especially in low data regime or in presence of low-quality inputs, the model risks learning sub-optimal decision boundaries, resulting in sub-optimal predictions. This dissertation presents novel data augmentation (DA) strategies aimed at improving the performance of machine learning models in low data regimes. The proposed techniques are designed to augment limited labeled datasets, providing the models with additional information to learn from.\\ The first contribution of this work is the development of Confidence-Guided Generative Augmentation (CGG-DA), a technique that trains and learns a generative model, such as Variational Autoencoder (VAE) and Deep Convolutional Generative Adversarial Networks (DCGAN), to generate synthetic augmentations. These generative models can generate labeled and/or unlabeled data by drawing from the same distribution as the under-performing samples based on a baseline reference model. By augmenting the training dataset with these synthetic images, CGG-DA aims to bridge the performance gap across different regions of the training feature space. We also introduce a Tool-Supported Contextual Augmentation (TSC-DA) technique that leverages existing ML models, such as classifiers or object detectors, to label available unlabeled data. Samples with consistent and high confidence predictions are used as labeled augmentations. On the other hand, samples with low confidence predictions might still contain some information even though they are more likely to be noisy and inconsistent. Hence, we keep them and use them as unlabeled samples during. Our third proposed DA explores the use of existing ML tools and external image repositories for data augmentation. This approach, called Guided External Data Augmentation (EG-DA), leverages external image repositories to augment the available dataset. External repositories are typically noisy, and might include a lot of out-of-distribution (OOD) samples. If included in the training process without proper handling, OOD samples can confuse the model and degrade the performance. To tackle this issue, we design and train a VAE-based anomaly detection component and use it to filter out any OOD samples. Since our DA includes both labeled data and a larger set of unlabeled data, we use semi-supervised training to exploit the information contained in the generated augmentations. This can guide the network to learn complex representations, and generalize to new data. The proposed data augmentation techniques are evaluated on two computer vision applications, and using multiple scenarios. We also compare our approach, using benchmark datasets, to baseline models trained on the initial labeled data only, and to existing data augmentation techniques. We show that each proposed augmentation consistently improve the results. We also perform an in-depth analysis to justify the observed improvements

Data driven discovery of materials properties.

The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning techniques for materials science applications. Guided by knowledge from domain experts, our approach focuses on machine learning to accelerate data-driven discovery of materials properties. Our objectives are two folds: (i) Identify the optimal set of features that best describes a given predicted variable. (ii) Boost prediction accuracy via applying various regression algorithms. Ordinary Least Square, Partial Least Square and Lasso regressions, combined with well adjusted feature selection techniques are applied and tested to predict key properties of semiconductors for two types of applications. First, we propose to build a more robust prediction model for band-gap energy (BG-E) of chalcopyrites, commonly used for solar cells industry. Compared to the results reported in [1-3] , our approach shows that learning and using only a subset of relevant features can improve the prediction accuracy by about 40%. For the second application, we propose to determine the underlying factors responsible for Defect-Induced Magnetism (DIM) in Dilute Magnetic Semiconductors (DMS) through the analysis of a set of 30 features for different DMS systems. We show that 8 of these features are more likely to contribute to this property. Using only these features to predict the total magnetic moment of new candidate DMSs has reduced the mean square error by about 90% compared to the models trained using the whole set of features. Given the scarcity of the available data sets for similar applications, this work aims not only to build robust models but also to establish a collaborative platform for future research

Confidence-Guided Data Augmentation for Deep Semi-Supervised Training

We propose a new data augmentation technique for semi-supervised learning settings that emphasizes learning from the most challenging regions of the feature space. Starting with a fully supervised reference model, we first identify low confidence predictions. These samples are then used to train a Variational AutoEncoder (VAE) that can generate an infinite number of additional images with similar distribution. Finally, using the originally labeled data and the synthetically generated labeled and unlabeled data, we retrain a new model in a semi-supervised fashion. We perform experiments on two benchmark RGB datasets: CIFAR-100 and STL-10, and show that the proposed scheme improves classification performance in terms of accuracy and robustness, while yielding comparable or superior results with respect to existing fully supervised approachesComment: 7 page